Integrative Bioinformatics Analysis Reveals New Prognostic Biomarkers of Clear Cell Renal Cell Carcinoma

Abstract

BACKGROUND: The outcome of clear cell renal cell carcinoma (ccRCC) is still unpredictable. Even with new targeted therapies, the average progression-free survival is dismal. Markers for early detection and progression could improve disease outcome. METHODS: To identify efficient and hitherto unrecognized pathogenic factors of the disease, we performed a uniquely comprehensive pathway analysis and built a gene interaction network based on large publicly available data sets assembled from 28 publications, comprising a 3-prong approach with high-throughput mRNA, microRNA, and protein expression profiles of 593 ccRCC and 389 normal kidney samples. We validated our results on 2 different data sets of 882 ccRCC and 152 normal tissues. Functional analyses were done by proliferation, migration, and invasion assays following siRNA (small interfering RNA) knockdown. RESULTS: After integration of multilevel data, we identified aryl-hydrocarbon receptor (AHR), grainyhead-like-2 (GRHL2), and KIAA0101 as new pathogenic factors. GRHL2 expression was associated with higher chances for disease relapse and retained prognostic utility after controlling for grade and stage [hazard ratio (HR), 3.47, P = 0.012]. Patients with KIAA0101-positive expression suffered worse disease-free survival (HR, 3.64, P < 0.001), and in multivariate analysis KIAA0101 retained its independent prognostic significance. Survival analysis showed that GRHL2- and KIAA0101-positive patients had significantly lower disease-free survival (P = 0.002 and P < 0.001). We also found that KIAA0101 silencing decreased kidney cancer cell migration and invasion in vitro. CONCLUSIONS: Using an integrative system biology approach, we identified 3 novel factors not previously linked to kidney cancer (AHR, GRHL2, and KIAA0101) that are involved in ccRCC pathogenesis and are potential biomarkers.